U.S. patent number 10,501,804 [Application Number 14/977,779] was granted by the patent office on 2019-12-10 for differential methylation level of cpg loci that are determinative of a biochemical reoccurrence of prostate cancer.
This patent grant is currently assigned to Board of Trustees of the Leland Stanford Junior University, HudsonAlpha Institute for Biotechnology. The grantee listed for this patent is Board of Directors of the Leland Stanford Junior University, HudsonAlpha Institute for Biotechnology. Invention is credited to James D. Brooks, Marie K. Kirby, Richard M. Myers.
United States Patent |
10,501,804 |
Myers , et al. |
December 10, 2019 |
Differential methylation level of CPG loci that are determinative
of a biochemical reoccurrence of prostate cancer
Abstract
The present disclosure provides for and relates to the
identification of novel biomarkers for diagnosis and prognosis of
prostate cancer or the biochemical reoccurrence of prostate cancer.
The biomarkers of the invention show altered methylation levels of
certain CpG loci relative to normal prostate tissue, as set
forth.
Inventors: |
Myers; Richard M. (Huntsville,
AL), Brooks; James D. (Stanford, CA), Kirby; Marie K.
(Huntsville, AL) |
Applicant: |
Name |
City |
State |
Country |
Type |
HudsonAlpha Institute for Biotechnology
Board of Directors of the Leland Stanford Junior
University |
Huntsville
Palo Alto |
AL
CA |
US
US |
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Assignee: |
HudsonAlpha Institute for
Biotechnology (Huntsville, AL)
Board of Trustees of the Leland Stanford Junior University
(Palo Alto, CA)
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Family
ID: |
51529818 |
Appl.
No.: |
14/977,779 |
Filed: |
December 22, 2015 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20160258023 A1 |
Sep 8, 2016 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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13829253 |
Mar 14, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q
1/6886 (20130101); C12Q 2600/154 (20130101); C12Q
2600/106 (20130101); C12Q 2600/112 (20130101) |
Current International
Class: |
C12Q
1/6886 (20180101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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Mar 2011 |
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Mar 2012 |
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2012138609 |
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2012174256 |
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2013033627 |
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Mar 2013 |
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WO |
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Other References
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.
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examiner .
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Patients With Post-Radical Prostatectomy Prostate Cancer" The
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applicant .
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Predictor of Decreased Risk of Recurrence Following Radical
Prostatectomy" The Prostate, 72:1133-1139 (2012). cited by
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Prevention" Cancer Epidemiol Biomarkers Prev 2012; 21:487-496; Jan.
13, 2012. cited by applicant .
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Prediction of Prostate Cancer" Clin Cancer Res 2012; 18:2882-2895.
cited by applicant .
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Genes That Have Potential Functional Consequences in Prostate
Cancer" PLoS ONE 7(10): e48455.doi:10.1371/journal.pone.0048455.
cited by applicant .
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10.1126/scitranslmed.3005211. cited by applicant .
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Region is a Strong Independent Prognostic Marker of Biochemical
Recurrence in Patients With Prostate Cancer After Radical
Prostatectomy" The Journal of Urology, vol. 181, 1678-1685, Apr.
2009. cited by applicant .
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levels of APC, TGFB2, HOXD3, and RASSF1A with prostate cancer
progression" Int. J. Cancer: 129, 2454-2462 (2011). cited by
applicant .
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hypermethylated in prostate cancer and predicts PSA recurrence"
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Silencing in Prostate Cancer Is an Independent Adverse Predictor of
Biochemical Recurrence after Radical Prostatectomy" Clin Cancer Res
2009; 15: 1400-1410. cited by applicant .
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(2011). cited by applicant .
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prostate cancer" BJU International; 105, 1364-1370 (2009). cited by
applicant .
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Coregulator MAGE-11 in Prostate Cancer by DNA Hypomethylation and
Cyclic AMP" Mol Cancer Res 2009; 7(4) Apr. 2009. cited by applicant
.
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Primary Examiner: Myers; Carla J
Attorney, Agent or Firm: Landau; Nicholas J. Bradley Arant
Boult Cummings LLP
Government Interests
STATEMENT OF GOVERNMENT INTEREST
This invention was made with government support under contract
W81XWH-10-1-0790 awarded by the Department of Defense. The
government has certain rights in this invention.
Parent Case Text
CROSS REFERENCE TO RELATED APPLICATIONS
This application is a continuation of, and therefore claims
priority to and the benefit of, pending U.S. patent application
Ser. No. 13/829,253 filed on Mar. 14, 2013 titled "DIFFERENTIAL
METHYLATION LEVEL OF CPG LOCI THAT ARE DETERMINATIVE OF A
BIOCHEMICAL REOCCURRENCE OF PROSTATE CANCER."
Claims
We claim:
1. A method for treating a biochemical reoccurrence of prostate
cancer in an individual, the method comprising: (a) identifying an
individual who has undergone a previous treatment for prostate
cancer, wherein said previous treatment comprises a radical
prostatectomy, and may be in need of treatment of the biochemical
reoccurrence of prostate cancer; (b) obtaining a sample from the
individual and isolating the DNA therefrom; (c) contacting the
isolated DNA with sodium bisulfite; (d) determining the methylation
level of at least one cytosine within a DNA region in the isolated
DNA where the DNA region is at least 90% identical to SEQ ID NO: 2
and determining the methylation level of at least one cytosine
within a DNA region in a sample from the individual where the DNA
region is at least 90% identical to SEQ ID NO: 1, wherein said
determining step comprises performing a molecular assay to
determine the methylation level of the at least one cytosine,
wherein said determining of the methylation level of the at least
one cytosine also comprises contacting the DNA region with a primer
that hybridizes to the DNA region; (e) comparing the methylation
level of the at least one cytosine to a threshold value for the at
least one cytosine, wherein the threshold value distinguishes
between individuals with and without a biochemical reoccurrence of
prostate cancer, wherein the comparison of the methylation level to
the threshold value is predictive of the presence or absence a
biochemical reoccurrence of prostate cancer in the individual; (f)
determining that the individual has a biochemical reoccurrence of
prostate cancer based on the compared methylation levels in step
(e); and (g) administering one or both of radiation therapy and
androgen deprivation therapy to the individual determined to have
the biochemical reoccurrence of prostate cancer in step (f), to
thereby treat the biochemical reoccurrence of prostate cancer.
2. The method of claim 1 wherein said sample is a biopsy
sample.
3. The method of claim 1 wherein said sample is a blood sample.
4. The method of claim 1 wherein said sample is a urine sample.
5. The method of claim 1 wherein the DNA region is at least 95%
identical to SEQ ID. NO. 2.
6. The method of claim 1 wherein step (d) comprises determining
whether cytosine residue at position 61 of SEQ ID NO: 2 is
methylated.
7. The method of claim 1 further comprising determining the
methylation level of cytosine in at least one of SEQ ID NOS:
3-32.
8. The method of claim 1 further comprising determining the
methylation level of cytosine in at least four of SEQ ID NOS:
3-32.
9. The method of claim 1 further comprising determining the
methylation level of cytosine in at least nine of SEQ ID NOS:
3-32.
10. The method of claim 6 wherein said sample is a biopsy
sample.
11. The method of claim 6 wherein said sample is a blood
sample.
12. The method of claim 6 wherein said sample is a urine sample.
Description
FIELD OF THE DISCLOSURE
The present invention relates to compositions and methods for
cancer diagnosis, research and therapy, including but not limited
to, cancer bio markers. In particular, the present invention
relates to methylation levels of certain CpG loci as prognostic and
diagnostic markers for prostate cancer or a biochemical recurrence
of prostate cancer.
BACKGROUND
Prostate cancer is the most commonly diagnosed malignancy for men
in the United States with an estimated 238,590 new cases projected
for 2013. The most current means for detecting prostate cancer is a
combination of a digital rectal exam (DRE) and monitoring levels of
prostate-specific antigen (PSA) in the blood. Prostate-specific
antigen is a protease produced by the prostate gland. PSA is
present at low concentration in the blood of healthy males, and an
increase in the concentration of PSA in the blood can be indicative
of a prostate tumor. Until recently, PSA testing was recommended as
a screening tool for all men over 50. However, two large-scale,
randomized trials of PSA screening suggest that prostate cancer is
over-diagnosed and over-treated, likely because many cancers that
are detected are never destined to progress. Prostate cancer can
have an aggressive and lethal course and an estimated 29,720 men
are projected to die of prostate cancer in 2013, however, for most
patients, prostate cancer is a slow growing disease. This broad
range of clinical behavior is likely a reflection of the underlying
genomic diversity of the tumors. Previous studies of prostate
tumors reported significant heterogeneity in the gene expression
profiles and genomic structural alterations including DNA copy
number changes and gene fusions often involving the ETS family of
transcription factors detectable in approximately half of prostate
tumors. Exon sequencing of known oncogenes and tumor suppressors
has found few somatic mutations and the calculated background
mutation rate appears to be relatively low. This suggests the
presence of other forms of genomic aberrations that contribute to
the observed gene expression variations, and in turn, the diversity
in tumor behavior.
Methods of detecting and/or diagnosing prostate cancer have been
described previously. See for instance the following issued U.S.
Pat. No. 7,524,633--Method of detection of prostate cancer; U.S.
Pat. No. 7,427,476--PITX2 polynucleotide, polypeptide and methods
of use therefore; U.S. Pat. No. 7,381,808 Method and nucleic acids
for the differentiation of prostate tumors; U.S. Pat. No.
7,252,935--Method of detection of prostate cancer; U.S. Pat. No.
7,195,870--Diagnosis of diseases associated with gene regulation;
U.S. Pat. No. 7,049,062--Assay for methylation in the GST-Pi gene;
U.S. Pat. No. 6,864,093--Method of identifying and treating
invasive carcinomas; U.S. Pat. No. 6,815,166--HIN-1, a tumor
suppressor gene; U.S. Pat. No. 6,783,933--CACNA1G polynucleotide,
polypeptide and methods of use therefore; U.S. Pat. No.
6,569,684--Method of identifying and treating invasive carcinomas;
U.S. Pat. No. 5,552,277--Genetic diagnosis of prostate cancer; and
U.S. Pat. No. 5,846,712 Tumor suppressor gene, HIC-1. In addition,
conventional methods utilize the prostate specific antigen (PSA)
blood test, and the digital rectal exam (DRE). PSA is an enzyme
produced in the prostate that is found in the seminal fluid and the
bloodstream. An elevated PSA level in the bloodstream does not
necessarily indicate prostate cancer, since PSA can also be raised
by infection or other prostate conditions such as benign prostatic
hyperplasia (BPH). Many men with an elevated PSA do not have
prostate cancer. Nonetheless, a PSA level greater than 4.0
nanograms per milliliter of serum was established initially as the
cutoff where the sensitivity for detecting prostate cancer was the
highest and the specificity for detecting non-cancerous conditions
was the lowest. A PSA level above 4.0 ng per milliliter of serum
may trigger a prostate biopsy to search for cancer. The digital
rectal exam is usually performed along with the PSA test, to check
for physical abnormalities that can result from tumor growth.
The PSA test is an imperfect screening tool. A man can have
prostate cancer and still have a PSA level in the "normal" range.
Approximately 25% of men who are diagnosed with prostate cancer
have a PSA level below 4.0. In addition, only 25% of men with a PSA
level of 4-10 are found to have prostate cancer. With a PSA level
exceeding 10, this rate jumps to approximately 65%.
Current diagnostic tools for prostate cancer lack the sensitivity
and specificity required for the detection of very early prostate
lesions and diagnosis ultimately relies on an invasive biopsy. Once
prostate cancer is diagnosed, there are no available prognostic
markers for prostate cancer that provide information on how
aggressively the tumor will grow. Therefore, more intrusive
therapeutic routes are often chosen that result in a drastic
reduction in the quality of life for the patient, even though the
majority of prostate tumors are slow growing and non-aggressive.
This ultimately leads to undue burden on the healthcare system and
an unnecessary decrease in quality of life for the patient. The
present invention addresses the need for distinguishing aggressive
prostate tumors through identification of specific genomic DNA
methylation biomarkers that can distinguish patients that will
undergo biochemical recurrence.
DNA methyltransferases (also referred to as DNA methylases)
transfer methyl groups from the universal methyl donor S-adenosyl
methionine to specific sites on a DNA molecule. Several biological
functions have been attributed to the methylated bases in DNA, such
as the protection of the DNA from digestion by restriction enzymes
in prokaryotic cells. In eukaryotic cells, DNA methylation is an
epigenetic method of altering DNA that influences gene expression,
for example during embryogenesis and cellular differentiation. The
most common type of DNA methylation in eukaryotic cells is the
methylation of cytosine residues that are 5' neighbors of guanine
("CU" dinucleotides, also referred to as "CpGs"). DNA methylation
regulates biological processes without altering genomic sequence.
DNA methylation regulates gene expression, DNA-protein
interactions, cellular differentiation, suppresses transposable
elements, and X Chomosome inactivation.
Improper methylation of DNA is believed to be the cause of some
diseases such as Beckwith-Wiedemann syndrome and Prader-Willi
syndrome. It has also been purposed that improper methylation is a
contributing factor in many cancers. For example, de novo
methylation of the Rb gene has been demonstrated in
retinoblastomas. In addition, expression of tumor suppressor genes
have been shown to be abolished by de novo DNA methylation of a
normally unmethylated 5' CpG island. Many additional effects of
methylation are discussed in detail in published International
Patent Publication No. WO 00/051639.
Methylation of cytosines at their carbon-5 position plays an
important role both during development and in tumorigenesis. Recent
work has shown that the gene silencing effect of methylated regions
is accomplished through the interaction of methylcytosine binding
proteins with other structural components of chromatin, which, in
turn, makes the DNA inaccessible to transcription factors through
histone deacetylation and chromatin structure changes. The
methylation occurs almost exclusively in CpG dinucleotides. While
the bulk of human genomic DNA is depleted in CpG sites, there are
CpG-rich stretches, so-called CpG islands, which are located in
promoter regions of more than 70% of all known human genes. In
normal cells, CpG islands are unmethylated, reflecting a
transcriptionally active state of the respective gene. Epigenetic
silencing of tumor suppressor genes by hypermethylation of CpG
islands is a very early and stable characteristic of tumorigenesis.
Hypermethylation of CpG islands located in the promoter regions of
tumor suppressor genes are now firmly established as the most
frequent mechanisms for gene inactivation in cancers.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 shows bar graphs of the percent methylation of each of the
predictive CpG loci in the biochemically recurrent patients and the
non-recurrent patients. B=biochemically recurrent patients,
N=patients that are not biochemically recurrent.
FIG. 2 shows the ROC curve for the best 3 CpG methylation
model+Gleason grade from the 18 best predictive CpG loci found
using linear regression (solid black line), the ROC curve for the
average of all possible 3 CpG loci models from the 18 CpGs (dashes
and circles), the ROC curve for Gleason grade alone (short dashes),
and the ROC curve for something with no predictive power (thin
black line). The ROC curve including both DNA methylation and
Gleason grade (solid black line) is statistically significantly
better (pval of 0.00031) at predicting patients who will
biochemically recur over Gleason grade alone (black dashes).
FIG. 3 shows the ROC curve models from the analysis of the
predictive CpGs discovered using survival analysis. The solid black
line shows the best predictive model of 3 CpG methylation
values+Gleason grade out of the 100 CpGs tested, and this is a
perfect predictor of recurrence in our dataset. The line with
dashes and circles represents the average of the 10 best models
from the 100 CpGs tested, the line with short dashes represents the
predictive power of Gleason grade alone, and the black line
represents a model with no predictive power.
SUMMARY
The present invention relates to the identification of novel
biomarkers for diagnosis and prognosis of prostate cancer. The
biomarkers of the invention are CpG loci that have altered
methylation levels relative to normal prostate tissue, as set
forth, for example, in Table 1. In one embodiment, the biomarkers
are indicative of the biochemical reoccurrence of prostate
cancer.
In some embodiments of the invention, the methylation level of one
or a plurality of biomarkers set forth in Table 1 is determined in
a patient sample suspected of comprising prostate cancer cells;
wherein altered methylation at the indicated biomarker is
indicative of prostate cancer or a biochemical recurrence of
prostate cancer. In some embodiments, a plurality of biomarkers is
evaluated for altered methylation.
In some embodiments the patient sample is a tumor biopsy. In other
embodiments the patient sample is a convenient bodily fluid, for
example a blood sample, urine sample, and the like. The biomarkers
of the present invention may further be combined with other
biomarkers for prostate cancer, including without limitation
prostate specific antigen, chromosome copy number alterations, and
the like.
DETAILED DESCRIPTION
Introduction
While the invention has been described with respect to a limited
number of embodiments, those skilled in the art, having benefit of
this disclosure, will appreciate that other embodiments can be
devised which do not depart from the scope of the invention as
disclosed here.
The present invention is based, in part, on the discovery that
sequences in certain DNA regions are methylated in cancer cells,
but not normal cells, or that methylation level at specific loci in
prostate cancer patients that undergo biochemical recurrence have a
different methylation level then the same loci in patients that do
not undergo recurrence. Specifically, the inventors have found that
methylation of biomarkers within the DNA regions described herein
(such as those identified in Table 1) are associated with prostate
cancer or the reoccurrence of prostate cancer.
In view of this discovery, the inventors have recognized that
methods for detecting the biomarker sequences and DNA regions
comprising the biomarker sequences as well as sequences adjacent to
the biomarkers that contain CpG loci subsequences, methylation
level of the DNA regions, and/or expression of the genes regulated
by the DNA regions can be used to predict recurrence of cancer
cells or to detect cancer cells. Detecting cancer cells allows for
diagnostic tests that detect disease, assess the risk of
contracting disease, determining a predisposition to disease, stage
disease, diagnosis of disease, monitor disease, and/or prognostic
biomarkers such as these methylation markers can be used to aid in
the selection of treatment for a patient after prostatectomy.
Definitions
Unless otherwise defined herein, scientific and technical terms
used in connection with the present invention shall have the
meanings that are commonly understood by those of ordinary skill in
the art. Further, unless otherwise required by context, singular
terms shall include pluralities and plural terms shall include the
singular. Generally, nomenclatures used in connection with, and
techniques of, cell and tissue culture, molecular biology,
immunology, microbiology, genetics and protein and nucleic acid
chemistry and hybridization described herein are those well known
and commonly used in the art. The methods and techniques of the
present invention are generally performed according to conventional
methods well known in the art and as described in various general
and more specific references that are cited and discussed
throughout the present specification unless otherwise indicated.
See, e.g., Sambrook et al. Molecular Cloning: A Laboratory Manual,
2d ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor,
N.Y. (1989) and Ausubel et al, Current Protocols in Molecular
Biology, Greene Publishing Associates (1992), and Harlow and Lane
Antibodies: A Laboratory Manual Cold Spring Harbor Laboratory
Press, Cold Spring Harbor, N.Y. (1990), which are incorporated
herein by reference. Enzymatic reactions and purification
techniques, if any, are performed according to manufacturer's
specifications, as commonly accomplished in the art or as described
herein. The terminology used in connection with, and the laboratory
procedures and techniques of, analytical chemistry, synthetic
organic chemistry, and medicinal and pharmaceutical chemistry
described herein are those well known and commonly used in the art.
Standard techniques can be used for chemical syntheses, chemical
analyses, pharmaceutical preparation, formulation, and delivery,
and treatment of patients.
The "Gleason" grading system is used to help evaluate the prognosis
of men with prostate cancer. Together with other parameters, it is
incorporated into a strategy of prostate cancer staging, which
predicts prognosis and helps guide therapy. A Gleason "score" or
"grade" is given to prostate cancer based upon its microscopic
appearance. Tumors with a low Gleason score typically grow slowly
enough that they may not pose a significant threat to the patients
in their lifetimes. These patients are monitored ("watchful
waiting" or "active surveillance") over time. Cancers with a higher
Gleason score are more aggressive and have a worse prognosis, and
these patients are generally treated with surgery (e.g., radical
prostectomy) and, in some cases, therapy (e.g., radiation, hormone,
ultrasound, chemotherapy).
The term "individual" or "patient" as used herein refers to any
animal, including mammals, such as, but not limited to, mice, rats,
other rodents, rabbits, dogs, cats, swine, cattle, sheep, horses,
primates, or humans.
The term "in need of prevention" as used herein refers to a
judgment made by a caregiver that a patient requires or will
benefit from prevention. This judgment is made based on a variety
of factors that are in the realm of a caregiver's expertise, and
may include the knowledge that the patient may become ill as the
result of a disease state that is treatable by a compound or
pharmaceutical composition of the disclosure.
The term "in need of treatment" as used herein refers to a judgment
made by a caregiver that a patient requires or will benefit from
treatment. This judgment is made based on a variety of factors that
are in the realm of a caregiver's expertise, and may include the
knowledge that the patient is ill as the result of a disease state
that is treatable by a compound or pharmaceutical composition of
the disclosure.
"Methylation" refers to cytosine methylation at positions C5 or N4
of cytosine, the N6 position of adenine or other types of nucleic
acid methylation. In vitro amplified DNA is unmethylated because in
vitro DNA amplification methods do not retain the methylation
pattern of the amplification template. However, "unmethylated DNA"
or "methylated DNA" can also refer to amplified DNA whose original
template was methylated or methylated, respectively.
The term "methylation level" as applied to a gene refers to whether
one or more cytosine residues present in a CpG context have or do
not have a methylation group. Methylation level may also refer to
the fraction of cells in a sample that do or do not have a
methylation group on such cytosines. Methylation level may also
alternatively describe whether a singe CpG di-nucleotide is
methylated.
A "methylation-dependent restriction enzyme" refers to a
restriction enzyme that cleaves or digests DNA at or in proximity
to a methylated recognition sequence, but does not cleave DNA at or
near the same sequence when the recognition sequence is not
methylated. Methylation-dependent restriction enzymes include those
that cut at a methylated recognition sequence (e.g., DpnI) and
enzymes that cut at a sequence near but not at the recognition
sequence (e.g., McrBC). For example, McrBC's recognition sequence
is 5' RmC (N40-3000) RmC 3' where "R" is a purine and "mC" is a
methylated cytosine and "N40-3000" indicates the distance between
the two RmC half sites for which a restriction event has been
observed. McrBC generally cuts close to one half-site or the other,
but cleavage positions are typically distributed over several base
pairs, approximately 30 base pairs from the methylated base. McrBC
sometimes cuts 3' of both half sites, sometimes 5' of both half
sites, and sometimes between the two sites. Exemplary
methylation-dependent restriction enzymes include, e.g., McrBC
(see, e.g., U.S. Pat. No. 5,405,760), McrA, MrrA, BisI, GlaI and
DpnI. One of skill in the art will appreciate that any
methylation-dependent restriction enzyme, including homologs and
orthologs of the restriction enzymes described herein, is also
suitable for use in the present invention.
A "methylation-sensitive restriction enzyme" refers to a
restriction enzyme that cleaves DNA at or in proximity to an
unmethylated recognition sequence but does not cleave at or in
proximity to the same sequence when the recognition sequence is
methylated. Exemplary methylation-sensitive restriction enzymes are
described in, e.g., McClelland et al., Nucleic Acids Res.
22(17):3640-59 (1994) and http://rebase.neb.com. Suitable
methylation-sensitive restriction enzymes that do not cleave DNA at
or near their recognition sequence when a cytosine within the
recognition sequence is methylated include, e.g., Aat II, Aci I,
Acl I, Age I, Alu I, Ase I, Ase I, AsiS I, Bbe I, BsaA I, BsaH I,
BsiE I, BsiW I, BsrF I, BssH II, BssK I, BstB I, BstN I, BstU I,
Cla I, Eae L, Eag L, Fau I, Fse I, Hha I, HinP1 I, HinC II, Hpa II,
Hpy99 I, HpyCH4 IV, Kas I, Mbo I, Mlu I, MapA1 I, Msp I, Nae I, Nar
I, Not I, Pml I, Pst I, Pvu I, Rsr II, Sac II, Sap I, Sau3A I, Sfl
I, Sfo I, SgrA I, Sma I, SnaB I, Tsc I, Xma I, and Zra I. Suitable
methylation-sensitive restriction enzymes that do not cleave DNA at
or near their recognition sequence when an adenosine within the
recognition sequence is methylated at position N.sup.6 include,
e.g., Mbo I. One of skill in the art will appreciate that any
methylation-sensitive restriction enzyme, including homologs and
orthologs of the restriction enzymes described herein, is also
suitable for use in the present invention. One of skill in the art
will further appreciate that a methylation-sensitive restriction
enzyme that fails to cut in the presence of methylation of a
cytosine at or near its recognition sequence may be insensitive to
the presence of methylation of an adenosine at or near its
recognition sequence. Likewise, a methylation-sensitive restriction
enzyme that fails to cut in the presence of methylation of an
adenosine at or near its recognition sequence may be insensitive to
the presence of methylation of a cytosine at or near its
recognition sequence. For example, Sau3AI is sensitive (i.e., fails
to cut) to the presence of a methylated cytosine at or near its
recognition sequence, but is insensitive (i.e., cuts) to the
presence of a methylated adenosine at or near its recognition
sequence. One of skill in the art will also appreciate that some
methylation-sensitive restriction enzymes are blocked by
methylation of bases on one or both strands of DNA encompassing of
their recognition sequence, while other methylation-sensitive
restriction enzymes are blocked only by methylation on both
strands, but can cut if a recognition site is hemi-methylated.
The term "prostate cancer" is used interchangeably and in the
broadest sense refers to all stages and all forms of cancer arising
from the tissue of the prostate gland.
The terms "peptide," "polypeptide," and "protein" each refer to a
molecule comprising two or more amino acid residues joined to each
other by peptide bonds. These terms encompass, e.g., native and
artificial proteins, protein fragments and polypeptide analogs such
as muteins, variants, and fusion proteins of a protein sequence as
well as post-translationally, or otherwise covalently or
non-covalently, modified proteins.
The terms "polynucleotide" and "nucleic acid" are used
interchangeably throughout and include DNA molecules (e.g., cDNA or
genomic DNA), RNA molecules (e.g., mRNA, siRNA), analogs of the DNA
or RNA generated using nucleotide analogs (e.g., peptide nucleic
acids and non-naturally occurring nucleotide analogs), and hybrids
thereof. The nucleic acid molecule can be single-stranded or
double-stranded. In one embodiment, the nucleic acid molecules of
the invention comprise a contiguous open reading frame encoding an
antibody, or a fragment, derivative, mutein, or variant thereof, of
the invention. The nucleic acids can be any length. They can be,
for example, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 75, 100, 125,
150, 175, 200, 250, 300, 350, 400, 450, 500, 750, 1,000, 1,500,
3,000, 5,000 or more nucleotides in length, and/or can comprise one
or more additional sequences, for example, regulatory sequences,
and/or be part of a larger nucleic acid, for example, a vector.
The terms "prevent", "preventing", "prevention" "suppress",
"suppressing" and "suppression" as used herein refer to
administering a compound either alone or as contained in a
pharmaceutical composition prior to the onset of clinical symptoms
of a disease state so as to prevent any symptom, aspect or
characteristic of the disease state. Such preventing and
suppressing need not be absolute to be useful.
The term "recurrence" is used herein to refer to local or distant
recurrence (i.e., metastasis) of cancer. For example, prostate
cancer can recur locally in the tissue next to the prostate or in
the seminal vesicles. The cancer may also affect the surrounding
lymph nodes in the pelvis or lymph nodes outside this area.
Prostate cancer can also spread to tissues next to the prostate,
such as pelvic muscles, bones, or other organs. Recurrence can be
determined by clinical recurrence detected by, for example, imaging
study or biopsy, or biochemical recurrence, which is defined by
detectable PSA levels in the blood after prostatectomy.
The term "therapeutically effective amount", in reference to the
treating, preventing or suppressing of a disease state, refers to
an amount of a compound either alone or as contained in a
pharmaceutical composition that is capable of having any
detectable, positive effect on any symptom, aspect, or
characteristics of the disease state/condition. Such effect need
not be absolute to be beneficial.
The terms "treat", "treating" and "treatment" as used herein refers
to administering a compound either alone or as contained in a
pharmaceutical composition after the onset of clinical symptoms of
a disease state so as to reduce or eliminate any symptom, aspect or
characteristic of the disease state. Such treating need not be
absolute to be useful.
DNA Methylation Level and Cancer
DNA methylation is a heritable, reversible and epigenetic change.
Yet, DNA methylation has the potential to alter gene expression,
which has profound developmental and genetic consequences. The
methylation reaction involves flipping a target cytosine out of an
intact double helix to allow the transfer of a methyl group from S
adenosyl-methionine in a cleft of the enzyme DNA
(cystosine-5)-methyltransferase to form 5-methylcytosine (5-mCyt).
This enzymatic conversion is the most common epigenetic
modification of DNA known to exist in vertebrates, and is essential
for normal embryonic development.
The presence of 5-mCyt at CpG dinucleotides has resulted in a
5-fold depletion of this sequence in the genome during vertebrate
evolution, presumably due to spontaneous deamination of 5-mCyt to
T. Those areas of the genome that do not show such suppression are
referred to as "CpG islands". These CpG island regions comprise
about 1% of vertebrate genomes and also account for about 15% of
the total number of CpG dinucleotides. CpG islands are typically
between 0.2 to about 1 kb in length and are located upstream of
many housekeeping and tissue-specific genes, but may also extend
into gene coding regions. Therefore, the methylation levels of
cytosine residues within CpG islands in somatic tissues can
modulate gene expression throughout the genome. Methylation levels
of cytosine residues contained within CpG islands of certain genes
has been inversely correlated with gene activity. Thus, methylation
of cytosine residues within CpG islands in somatic tissue is
generally associated with decreased gene expression and can affect
a variety of mechanisms including, for example, disruption of local
chromatin structure, inhibition of transcription factor-DNA
binding, or by recruitment of proteins which interact specifically
with methylated sequences indirectly preventing transcription
factor binding. Despite a generally inverse correlation between
methylation of CpG islands and gene expression, most CpG islands on
autosomal genes remain unmethylated in the germline and methylation
of these islands is usually independent of gene expression.
Tissue-specific genes are usually unmethylated at the receptive
target organs but are methylated in the germline and in
non-expressing adult tissues. CpG islands of
constitutively-expressed housekeeping genes are normally
unmethylated in the germline and in somatic tissues. A recent study
showed evidence that methylation status of CpGs located within 2000
base pairs of a gene's transcription start site is negatively
correlated with gene expression. For CpGs within a gene body, the
methylation status of CpGs not in CpG islands is positively
correlated with gene expression, whereas CpGs in the gene body in
CpG islands can both negatively and positively impact gene
expression (Varley et al, 2013).
Abnormal methylation of CpG islands associated with tumor
suppressor genes can cause altered gene expression. Increased
methylation (hypermethylation) of such regions can lead to
progressive reduction of normal gene expression resulting in the
selection of a population of cells having a selective growth
advantage. Conversely, decreased methylation (hypomethylation) of
oncogenes can lead to modulation of normal gene expression
resulting in the selection of a population of cells having a
selective growth advantage. In some examples, hypermethylation
and/or hypomethylation of one or more CpG dinucleotide is
considered to be abnormal methylation.
Biomarkers
The present disclosure provides biomarkers useful for the detection
of the prostate cancer or reoccurence of prostate cancer, wherein
the methlyation level of the biomarker is indicative of the
reoccurence of prostate cancer. In one embodiment, the methylation
level is determined by a cytosine. In one embodiment, the
biomarkers are associated with certain genes in an individual. In
one embodiment, the biomarkers are associated with certain CpG
loci. In one embodiment, the CpG loci may be located in the
promoter region of a gene, in an intron or exon of a gene or
located near the gene in a patient's genomic DNA. In an alternate
embodiment, the CpG may not be associated with any known gene or
may be located in an intergenic region of a chromosome. In some
embodiments, the CpG loci may be associated with one or more than
one gene.
In one embodiment, the gene associated with the biomarker is
ADPRHL1 which is also referred to as ADP-ribosylhydrolase like 1.
In one embodiment, the CpG loci are cg00474017 or cg05387119.
In an alternate embodiment, the gene associated with the biomarker
is ZNF787 which is also referred to as zinc finger protein 787, TIP
20 and TTF-I-interacting peptide 20. In one embodiment, the CpG
locus is cg06161930.
In an alternate embodiment, the gene associated with the biomarker
gene is SHISA9 which is also referred to as CKAMP44 and
cystine-knot AMPAR moduclating protein. In one embodiment, the CpG
locus is cg06345462.
In yet an alternate embodiment, the gene associated with the
biomarker gene is FLI1 also known as friend leukemia integration 1
transcription factor, proto-oncogene Fli-1 or transcription factor
ERGB. In one embodiment, the CpG locus is cg11017065.
In yet an alternate embodiment, the gene associated with the
biomarker is SNX8 which is also known as sorting nexin 8 and Mvp1.
In one embodiment, the CpG locus is cg13641082.
In an alternate embodiment, the gene associated with the biomarker
is FANCC which is also known as protein FACC, Fanconi anemia,
complementation group C and FA3. In one embodiment, the CpG locus
is cg14127626.
In an alternate embodiment, the gene associated with the biomarker
is TMEM79 which is also known as transmembrane protein 79. In one
embodiment, the CpG locus is cg18973101.
In an alternate embodiment, the gene associated with the biomarker
is SMG5 which is also known as EST1B, PLTS-RP1 and SMG-5. In one
embodiment, the CpG locus is cg18973101.
In an alternate embodiment, the gene associated with the biomarker
is RGAG1 which is also known as MAR9 and retrotransposon gag domain
containing 1. In one embodiment, the CpG locus is cg20522409. This
CpG locus is on the X chromosome.
In an alternate embodiment, the gene associated with the biomarker
is AMMECR1 which is also known as Alport syndrome, mental
retardation, midface hypoplasia and elliptocytosis chromosomal
region gene 1. In one embodiment, the CpG locus is cg20522409. This
CpG locus is on the X chromosome.
In an alternate embodiment, the gene associated with the biomarker
is TIMMDC1 which is also known as translocase of inner
mitochondrial membrane domain containing 1. In one embodiment, the
CpG locus is cg21139795.
In an alternate embodiment, the gene associated with the biomarker
is CD80 which is also known as B7-1 and BB1. In one embodiment, the
CpG locus is cg21139795.
In an alternate embodiment, the gene associated with the biomarker
is MYT1L which is also known as myelin transcription factor 1-like
protein and NZF01. In one embodiment, the CpG locus is
cg21741679.
In an alternate embodiment, the gene associated with the biomarker
is BCLAF1 which is also known as BTF. In one embodiment, the CpG
locus is cg21889703.
In an alternate embodiment, the gene associated with the biomarker
is ARHGEF7 which is also known as COOL-1, p85 and PIXB. In one
embodiment, the CpG locus is cg22032283.
In an alternate embodiment, the gene associated with the biomarker
is C10orf28 which is also known as PSORT and R3H domain and
coiled-coli containing 1-like. In one embodiment, the CpG locus is
cg26450259.
In an alternate embodiment, the gene associated with the biomarker
is LOC348021. In one embodiment, the CpG locus is cg27252467.
In an alternate embodiment, the gene associated with the biomarker
is TBPL1 which is also known as STUD and TLF. In one embodiment,
the CpG locus is cg00004608.
In an alternate embodiment, the gene associated with the biomarker
is CBFA2T3 which is also known as MTG16. In one embodiment, the CpG
locus is cg00493358.
In an alternate embodiment, the gene associated with the biomarker
is ZNF276 which is also known as zinc finger protein 276. In one
embodiment, the CpG locus is cg07221183.
In an alternate embodiment, the gene associated with the biomarker
is ZNF19 which is also known as zinc finger 19 protein. In one
embodiment, the CpG locus is cg07506795.
In an alternate embodiment, the gene associated with the biomarker
is PDGFC which is also known as Platelet-derived growth factor C.
In one embodiment, the CpG locus is cg07537734.
In an alternate embodiment, the gene associated with the biomarker
is HLA-DPB2 whish is also known as DPB. In one embodiment, the CpG
locus is cg11786476.
In an alternate embodiment, the gene associated with the biomarker
is EXD3 which is also known as exonuclease 3'-5' domain containing
3 and mut-7. In one embodiment, the CpG locus is cg13916516.
In an alternate embodiment, the gene associated with the biomarker
is WWC1 is also known as KIBRA. In one embodiment, the CpG locus is
cg18472912.
In an alternate embodiment, the gene associated with the biomarker
is PRDM16 which is also known as MEL1, PR domain containing 16 and
KIAA1675. In one embodiment, the CpG locus is cg23821340.
In an alternate embodiment, the gene associated with the biomarker
is CNGA3 which is also known as CNG3. In one embodiment, the CpG
locus is cg24778248.
In an alternate embodiment, the gene associated with the biomarker
is MEGF8 which is also known as SBP1. In one embodiment, the CpG
locus is cg26548653.
In an alternate embodiment, the gene associated with the biomarker
is TMEM145 which is also known as transmembrane protein 145. In one
embodiment, the CpG locus is cg26548653.
In one embodiment, the CpG locus is cg19480425 located on
chromosome 22. In one embodiment, the CpG locus is cg20077773
located on chromosome 12. In one embodiment, the CpG locus is
cg26204682 located on chromosome 4. In one embodiment, the CpG
locus is cg17881513 located on chromosome 8. In one embodiment, the
CpG locus is cg18516946 located on chromosome 11. In one
embodiment, the CpG locus is cg24773418 located on chromosome
14.
In one embodiment, the methylation level of one (1) of the
following CpG loci may be determined (by any method set forth
herein) to determine whether an individual is or may be at a risk
for prostate cancer or a biochemical reoccurence of prostate
cancer: cg00474017, cg05387119, cg06161930, cg11017065, cg1364108,
cg14127626, cg18973101, cg19480425, cg20077773, cg20522409,
cg21889703, cg22032283, cg26204682, cg06345462, cg21139795,
cg21741679, cg26450259 and cg27252467. In some aspects, the
methylation level of two (2) or more or three (3) or more of the
forgoing CpG loci may be determined (by any method set forth
herein) to determine whether an individual is or may be at a risk
for prostate cancer or a biochemical reoccurence of prostate
cancer.
In some aspects, the methylation level of any one of the following
biomarkers and associated genes may be determined (by any method
set forth herein) to determine whether an individual is or may be
at a risk for prostate cancer or a biochemical reoccurence of
prostate cancer: ADPRHL1, AMMECR1, RGAG1, ZNF787, FLI1, SNX8,
FANCC, SMG5, MEM79, BCLAF1, ARHGEF7, ZNF19, C10orf28, SHISA9,
MYT1L, LOC348021. In some aspects, the methylation level of two (2)
or more or three (3) or more of the forgoing biomarkers be
determined (by any method set forth herein) to determine whether a
patient is or may be at a risk for prostate cancer or a biochemical
reoccurence of prostate cancer.
In one embodiment, an increase in the methylation level of one or
more of the following CpG loci is indicative of prostate cancer or
the biochemical reoccurrence of prostate cancer: cg06161930,
cg13641082, cg19480425, cg20077773, cg21889703, cg06345462,
cg21139795, cg21741679, cg00004608, cg07537734, cg18472912,
cg24773418, cg24778248 and cg26548653.
In one embodiment, a decrease in the methylation level of one or
more of the following CpG loci is indicative of prostate cancer or
the biochemical reoccurrence of prostate cancer: cg00474017,
cg05387119, cg11017065, cg18973101, cg20522409, cg26204682,
cg26450259, cg00493358, cg07221183, cg07506795, cg11786476,
cg13916516, cg18516946, cg17881513 and cg23821340.
Table 1 shows the CpG loci, their chromosomal position (if known),
and the genes associated with the CpG loci:
TABLE-US-00001 TABLE 1 The biomarkers of the present disclosure.
Position in Human Chromo- Associated Genome 19 CpG loci some
Gene(s) (hg19) SEQ ID NO. cg00474017 13 ADPRHL1 114074435 SEQ ID
NO. 1 cg05387119 13 ADPRHL1 114074465 SEQ ID NO. 2 cg06161930 19
ZNF787 56633191 SEQ ID NO. 3 cg06345462 16 SHISA9 13263104 SEQ ID
NO. 4 cg11017065 11 FLI1 128564874 SEQ ID NO. 5 cg13641082 7 SNX8
2319604 SEQ ID NO. 6 cg14127626 9 FANCC 98075481 SEQ ID NO. 7
cg18973101 1 SMG5; 156251280 SEQ ID NO. 8 TMEM79 cg19480425 22 NA
22339538 SEQ ID NO. 9 cg20077773 12 NA 46851689 SEQ ID NO. 10
cg20522409 X AMMECR; 109661602 SEQ ID NO. 11 RGAG1 cg21139795 3
CD80; 119243933 SEQ ID NO. 12 TIMMDC1 cg21741679 2 MYT1L 2176774
SEQ ID NO. 13 cg21889703 6 BCLAF1 136607649 SEQ ID NO. 14
cg22032283 13 ARHGEF7 111936044 SEQ ID NO. 15 cg26204682 4 NA
105781484 SEQ ID NO. 16 cg26450259 10 C10orf28 99912042 SEQ ID NO.
17 cg27252467 13 LOC348021 19585665 SEQ ID NO. 18 cg00004608 6
TBPL1 134272463 SEQ ID NO. 19 cg00493358 16 CBFA2T3 88980724 SEQ ID
NO. 20 cg07221183 16 ZNF276 89800359 SEQ ID NO. 21 cg07506795 16
ZNF19 71523560 SEQ ID NO. 22 cg07537734 4 PDGFC 157893541 SEQ ID
NO. 23 cg11786476 6 HLA-DPB2 33096738 SEQ ID NO. 24 cg13916516 9
EXD3 140268774 SEQ ID NO. 25 cg17881513 8 NA 10717687 SEQ ID NO. 26
cg18472912 5 WWC1 167799541 SEQ ID NO. 27 cg18516946 11 NA 94774414
SEQ ID NO. 28 cg23821340 1 PRDM16 3303053 SEQ ID NO. 29 cg24773418
14 NA 33402512 SEQ ID NO. 30 cg24778248 2 CNGA3 98963062 SEQ ID NO.
31 cg26548653 19 TMEM145; 42829042 SEQ ID NO. 32 MEGF8 The "CpG
loci" column is the reference number provided by Illumina's .RTM.
Golden Gate and Infinium .RTM. Assays. The "position" column are
the genomic positions that correspond to the most current knowledge
of the human genome sequence, which is the Human Feburary 2009
assembly known as GRCh37/hg19. The nucleotide sequences of the CpG
loci in Table 1 are shown in Table 2 as well as the sequence
listing filed herewith.
Use of Biomarkers
In some embodiments, the methylation level of the chromosomal DNA
within a DNA region or portion thereof (e.g., at least one cytosine
residue) selected from the CpG loci identified in Table 1 is
determined. In some embodiments, the methylation level of all
cytosines within at least 20, 50, 100, 200, 500 or more contiguous
base pairs of the CpG loci is also determined. For example, in one
embodiment, the methylation level of the cytosine at cg18472912 is
determined. In some embodiments, pluralities of CpG loci are
assessed and their methylation level determined.
In some embodiments of the invention, the methylation level of a
CpG loci is determined and then normalized (e.g., compared) to the
methylation of a control locus. Typically the control locus will
have a known, relatively constant, methylation level. For example,
the control sequence can be previously determined to have no, some
or a high amount of methylation (or methylation level), thereby
providing a relative constant value to control for error in
detection methods, etc., unrelated to the presence or absence of
cancer. In some embodiments, the control locus is endogenous, i.e.,
is part of the genome of the individual sampled. For example, in
mammalian cells, the testes-specific histone 2B gene (hTH2B in
human) gene is known to be methylated in all somatic tissues except
testes. Alternatively, the control locus can be an exogenous locus,
i.e., a DNA sequence spiked into the sample in a known quantity and
having a known methylation level.
The methylation sites in a DNA region can reside in non-coding
transcriptional control sequences (e.g. promoters, enhancers, etc.)
or in coding sequences, including introns and exons of the
associated genes. In some embodiments, the methods comprise
detecting the methylation level in the promoter regions (e.g.,
comprising the nucleic acid sequence that is about 1.0 kb, 1.5 kb,
2.0 kb, 2.5 kb, 3.0 kb, 3.5 kb or 4.0 kb 5' from the
transcriptional start site through to the transcriptional start
site) of one or more of the associated genes identified in Table
1.
Any method for detecting methylation levels can be used in the
methods of the present invention.
In some embodiments, methods for detecting methylation levels
include randomly shearing or randomly fragmenting the genomic DNA,
cutting the DNA with a methylation-dependent or
methylation-sensitive restriction enzyme and subsequently
selectively identifying and/or analyzing the cut or uncut DNA.
Selective identification can include, for example, separating cut
and uncut DNA (e.g., by size) and quantifying a sequence of
interest that was cut or, alternatively, that was not cut.
Alternatively, the method can encompass amplifying intact DNA after
restriction enzyme digestion, thereby only amplifying DNA that was
not cleaved by the restriction enzyme in the area amplified. In
some embodiments, amplification can be performed using primers that
are gene specific. Alternatively, adaptors can be added to the ends
of the randomly fragmented DNA, the DNA can be digested with a
methylation-dependent or methylation-sensitive restriction enzyme,
intact DNA can be amplified using primers that hybridize to the
adaptor sequences. In this case, a second step can be performed to
determine the presence, absence or quantity of a particular gene in
an amplified pool of DNA. In some embodiments, the DNA is amplified
using real-time, quantitative PCR.
In some embodiments, the methods comprise quantifying the average
methylation density in a target sequence within a population of
genomic DNA. In some embodiments, the method comprises contacting
genomic DNA, with a methylation-dependent restriction enzyme or
methylation-sensitive restriction enzyme under conditions that
allow for at least some copies of potential restriction enzyme
cleavage sites in the locus to remain uncleaved; quantifying intact
copies of the locus; and comparing the quantity of amplified
product to a control value representing the quantity of methylation
of control DNA, thereby quantifying the average methylation density
in the locus compared to the methylation density of the control
DNA.
The methylation level of a CpG loci can be determined by providing
a sample of genomic DNA comprising the CpG locus, cleaving the DNA
with a restriction enzyme that is either methylation-sensitive or
methylation-dependent, and then quantifying the amount of intact
DNA or quantifying the amount of cut DNA at the locus of interest.
The amount of intact or cut DNA will depend on the initial amount
of genomic DNA containing the locus, the amount of methylation in
the locus, and the number (i.e., the fraction) of nucleotides in
the locus that are methylated in the genomic DNA. The amount of
methylation in a DNA locus can be determined by comparing the
quantity of intact DNA or cut DNA to a control value representing
the quantity of intact DNA or cut DNA in a similarly-treated DNA
sample. The control value can represent a known or predicted number
of methylated nucleotides. Alternatively, the control value can
represent the quantity of intact or cut DNA from the same locus in
another (e.g., normal, non-diseased) cell or a second locus.
By using at least one methylation-sensitive or
methylation-dependent restriction enzyme under conditions that
allow for at least some copies of potential restriction enzyme
cleavage sites in the locus to remain uncleaved and subsequently
quantifying the remaining intact copies and comparing the quantity
to a control, average methylation density of a locus can be
determined. If the methylation-sensitive restriction enzyme is
contacted to copies of a DNA locus under conditions that allow for
at least some copies of potential restriction enzyme cleavage sites
in the locus to remain uncleaved, then the remaining intact DNA
will be directly proportional to the methylation density, and thus
may be compared to a control to determine the relative methylation
density of the locus in the sample. Similarly, if a
methylation-dependent restriction enzyme is contacted to copies of
a DNA locus under conditions that allow for at least some copies of
potential restriction enzyme cleavage sites in the locus to remain
uncleaved, then the remaining intact DNA will be inversely
proportional to the methylation density, and thus may be compared
to a control to determine the relative methylation density of the
locus in the sample.
Kits for the above methods can include, e.g., one or more of
methylation-dependent restriction enzymes, methylation-sensitive
restriction enzymes, amplification (e.g., PCR) reagents, probes
and/or primers.
Quantitative amplification methods (e.g., quantitative PCR or
quantitative linear amplification) can be used to quantify the
amount of intact DNA within a locus flanked by amplification
primers following restriction digestion. Methods of quantitative
amplification are disclosed in, e.g., U.S. Pat. Nos. 6,180,349;
6,033,854; and 5,972,602. Amplifications may be monitored in "real
time."
Additional methods for detecting methylation levels can involve
genomic sequencing before and after treatment of the DNA with
bisulfite. When sodium bisulfite is contacted to DNA, unmethylated
cytosine is converted to uracil, while methylated cytosine is not
modified. Such additional embodiments include the use of
array-based assays such as the Illumina.RTM. Human Methylation450
BeadChip and multi-plex PCR assays. In one embodiment, the
multi-plex PCR assay is Patch PCR. PatchPCR can be used to
determine the methylation level of a certain CpG loci. See Varley
KE and Mitra RD (2010). Bisulfite Patch PCR enables multiplexed
sequencing of promoter methylation across cancer samples. Genome
Research. 20:1279-1287.
In some embodiments, restriction enzyme digestion of PCR products
amplified from bisulfite-converted DNA is used to detect DNA
methylation levels.
In some embodiments, a "MethyLight" assay is used alone or in
combination with other methods to detect methylation level.
Briefly, in the MethyLight process, genomic DNA is converted in a
sodium bisulfite reaction (the bisulfite process converts
unmethylated cytosine residues to uracil). Amplification of a DNA
sequence of interest is then performed using PCR primers that
hybridize to CpG dinucleotides. By using primers that hybridize
only to sequences resulting from bisulfite conversion of
unmethylated DNA, (or alternatively to methylated sequences that
are not converted) amplification can indicate methylation status of
sequences where the primers hybridize. Similarly, the amplification
product can be detected with a probe that specifically binds to a
sequence resulting from bisulfite treatment of a unmethylated (or
methylated) DNA. If desired, both primers and probes can be used to
detect methylation status. Thus, kits for use with MethyLight can
include sodium bisulfite as well as primers or detectably-labeled
probes (including but not limited to Taqman or molecular beacon
probes) that distinguish between methylated and unmethylated DNA
that have been treated with bisulfite. Other kit components can
include, e.g., reagents necessary for amplification of DNA
including but not limited to, PCR buffers, deoxynucleotides; and a
thermostable polymerase.
In some embodiments, a Ms-SNuPE (Methylation-sensitive Single
Nucleotide Primer Extension) reaction is used alone or in
combination with other methods to detect methylation level. The
Ms-SNuPE technique is a quantitative method for assessing
methylation differences at specific CpG sites based on bisulfite
treatment of DNA, followed by single-nucleotide primer extension.
Briefly, genomic DNA is reacted with sodium bisulfite to convert
unmethylated cytosine to uracil while leaving 5-methylcytosine
unchanged. Amplification of the desired target sequence is then
performed using PCR primers specific for bisulfite-converted DNA,
and the resulting product is isolated and used as a template for
methylation analysis at the CpG site(s) of interest.
Typical reagents (e.g., as might be found in a typical
Ms-SNuPE-based kit) for Ms-SNuPE analysis can include, but are not
limited to: PCR primers for specific gene (or methylation-altered
DNA sequence or CpG island); optimized PCR buffers and
deoxynucleotides; gel extraction kit; positive control primers;
Ms-SNuPE primers for a specific gene; reaction buffer (for the
Ms-SNuPE reaction); and detectably-labeled nucleotides.
Additionally, bisulfite conversion reagents may include: DNA
denaturation buffer; sulfonation buffer; DNA recovery regents or
kit (e.g., precipitation, ultrafiltration, affinity column);
desulfonation buffer; and DNA recovery components.
In some embodiments, a methylation-specific PCR ("MSP") reaction is
used alone or in combination with other methods to detect DNA
methylation. An MSP assay entails initial modification of DNA by
sodium bisulfite, converting all unmethylated, but not methylated,
cytosines to uracil, and subsequent amplification with primers
specific for methylated versus unmethylated DNA.
Additional methylation level detection methods include, but are not
limited to, methylated CpG island amplification and those described
in, e.g., U.S. Patent Publication 2005/0069879; Rein, et al.
Nucleic Acids Res. 26 (10): 2255-64 (1998); Olek, et al. Nat.
Genet. 17(3): 275-6 (1997); and PCT Publication No. WO
00/70090.
Kits
This invention also provides kits for the detection and/or
quantification of the diagnostic biomarkers of the invention, or
expression or methylation level thereof using the methods described
herein.
For Kits for detection of methylation level can comprise at least
one polynucleotide that hybridizes to one of the CpG loci
identified in Table 1 (or a nucleic acid sequence at least 90%
identical to the CpG loci of Tale 1), or that hybridizes to a
region of DNA flanking one of the CpG identified in Table 1, and at
least one reagent for detection of gene methylation. Reagents for
detection of methylation include, e.g., sodium bisulfite,
polynucleotides designed to hybridize to sequence that is the
product of a biomarker sequence of the invention if the biomarker
sequence is not methylated, and/or a methylation-sensitive or
methylation-dependent restriction enzyme. The kits can provide
solid supports in the form of an assay apparatus that is adapted to
use in the assay. The kits may further comprise detectable labels,
optionally linked to a polynucleotide, e.g., a probe, in the kit.
Other materials useful in the performance of the assays can also be
included in the kits, including test tubes, transfer pipettes, and
the like. The kits can also include written instructions for the
use of one or more of these reagents in any of the assays described
herein.
In some embodiments, the kits of the invention comprise one or more
(e.g., 1, 2, 3, 4, or more) different polynucleotides (e.g.,
primers and/or probes) capable of specifically amplifying at least
a portion of a DNA region where the DNA region includes one of the
CpG Loci identified in Table 1. Optionally, one or more
detectably-labeled polypeptides capable of hybridizing to the
amplified portion can also be included in the kit. In some
embodiments, the kits comprise sufficient primers to amplify 2, 3,
4, 5, 6, 7, 8, 9, 10, or more different DNA regions or portions
thereof, and optionally include detectably-labeled polynucleotides
capable of hybridizing to each amplified DNA region or portion
thereof. The kits further can comprise a methylation-dependent or
methylation sensitive restriction enzyme and/or sodium
bisulfite.
Methods of Diagnosis and Methods of Treatment
The present disclosure provides methods for the treatment and/or
prevention of a disease state that is characterized, at least in
part, by the altered methylation level of the CpG loci identified
in Table 1.
In one embodiment, the altered methylation at CpG loci are
associated with the occurrence in a patient of a cancer. In one
embodiment, the cancer is prostate cancer. In one embodiment, the
altered methylation levels of the CpG loci are associated with the
reoccurrence of prostate cancer. In one embodiment, the altered
methylation levels of the CpG loci is differentially diagnostic in
a patient suffering from prostate cancer as compared to a patient
not suffering from prostate cancer.
As illustrated in FIGS. 1A-3, determining the methylation levels of
at least one of the CpG loci identified in Table 1 is predictive of
prostate cancer or the recurrence of prostate cancer. FIG. 1 shows
that shows bar graphs of the percent methylation of each of the CpG
loci in the biochemically recurrent patients and the non-recurrent
patient where "B" is used for patients with a biochemical
recurrence of prostate cancer and "N" is used for patients without
a biochemical recurrence of prostate cancer.
FIG. 2 shows the ROC curve for the best 3 CpG methylation
model+Gleason grade from the 18 CpGs found using linear regression
(solid black line), the ROC curve for the average of all possible 3
CpG models from the 18 CpGs (dashes and circles), the ROC curve for
Gleason grade alone (short dashes), and the ROC curve for something
with no predictive power (thin black line). The ROC curve including
both DNA methylation and Gleason grade (solid black line) is
statistically significantly better (pval of 0.00031) at predicting
patients who will biochemically recur over Gleason grade alone
(black dashes).
FIG. 3 shows the ROC curve models from the analysis of the
predictive CpGs discovered using survival analysis. The solid black
line shows the best predictive model of 3 CpG methylation
values+Gleason grade out of the 100 CpGs tested, and this is a
perfect predictor of recurrence in our dataset. The line with
dashes and circles represents the average of the 10 best models
from the 100 CpGs tested, the line with short dashes represents the
predictive power of Gleason grade alone, and the black line
represents a model with no predictive power.
Other non-limiting methods of diagnosis and treatment are described
below. In this embodiment, the methylation levels of the CpG loci
identified in Table 1 is detected to aid in the treatment,
prevention or diagnosis of a cancer, such as prostate cancer.
The steps in the method of treatment or prevention, in one
embodiment are:
A. Identifying a patient in need of the prevention or treatment of
prostate cancer. This identifying step may be accomplished by many
different methods. The patient could be identified by a physician
who believes the patient would benefit from such treatment
prevention or by standard genetic screening or analysis indicating
the patient would benefit from such treatment or prevention.
B. Obtaining a sample from the patient. In some embodiments the
patient sample is a tumor biopsy. In other embodiments the patient
sample is a convenient bodily fluid, for example a blood sample,
urine sample, and the like. The sample may be obtained by other
means as well.
C. Determining the methylation levels of one or more of the CpG
loci or dinculetides at the Hg19 positions identified on Table 1.
This determination step may be accomplished by any of the means set
forth in this disclosure. In one embodiment, the methylation level
of one of the CpG loci is determined while in other embodiments,
the methylation levels of a plurality of the CpG loci are
determined. Additionally, other tests may be used in conjunction
with this determining step, including without limitation PSA assays
and the Gleason score.
D. Comparing the methylation levels of CpG loci determined in step
"C" to a reference or control. In one embodiment, a methylation
level of the CpG loci determined in step "C" different from the
control is indicative of the reoccurrence of prostate cancer. This
comparison step may be accomplished by any of the methods set forth
herein.
E. Treating the patient with a therapeutically effective amount of
a composition or radiation therapy if the comparing step in "D"
above indicates the reoccurrence of prostate cancer. In one
embodiment, the composition may include compounds for hormone
therapy such as androgen deprivation therapy.
In one embodiment, the method of treatment or prevention above is
used if the patient has previously undergone treatment, such as
radiation, a prostatectomy or hormone treatment for prostate cancer
and a reoccurrence of prostate cancer is feared.
In an alternate embodiment, the present invention provides methods
for determining the methylation status of an individual. In one
aspect, the methods comprise obtaining a biological sample from an
individual; and determining the methylation level of at least one
cytosine within a DNA region in a sample from an individual where
the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,
98%, or 99% identical to, or comprises, a sequence selected from
the group consisting of SEQ ID NOS.: 1-32.
In some embodiments, the methods comprise: A. Determining the
methylation status of at least one cytosine within a DNA region in
a sample from the individual where the DNA region is at least 90%,
91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or
comprises, a sequence selected from the group consisting of SEQ ID
NOS.: 1-32 and B. Comparing the methylation status of the at least
one cytosine to a threshold value for the biomarker, wherein the
threshold value distinguishes between individuals with and without
cancer, wherein the comparison of the methylation status to the
threshold value is predictive of the presence or absence of
prostate cancer in the individual.
In some embodiments, the methods comprise: A. Determining the
methylation status of at least one cytosine within a DNA region in
a sample from the individual where the DNA region is at least 90%,
91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or
comprises, a sequence selected from the group consisting of SEQ ID
NOS.: 1-32 and B. Comparing the methylation status of the at least
one cytosine to a threshold value for the biomarker, wherein the
threshold value distinguishes between individuals with and without
cancer, wherein the comparison of the methylation status to the
threshold value is predictive of the biochemical reoccurence of
prostate cancer in the individual. Computer-Based Methods
The calculations for the methods described herein can involve
computer-based calculations and tools. For example, a methylation
level for a DNA region or a CpG loci can be compared by a computer
to a threshold value, as described herein. The tools are
advantageously provided in the form of computer programs that are
executable by a general purpose computer system (referred to herein
as a "host computer") of conventional design. The host computer may
be configured with many different hardware components and can be
made in many dimensions and styles (e.g., desktop PC, laptop,
tablet PC, handheld computer, server, workstation, mainframe).
Standard components, such as monitors, keyboards, disk drives, CD
and/or DVD drives, and the like, may be included. Where the host
computer is attached to a network, the connections may be provided
via any suitable transport media (e.g., wired, optical, and/or
wireless media) and any suitable communication protocol (e.g.,
TCP/IP); the host computer may include suitable networking hardware
(e.g., modem, Ethernet card, WiFi card). The host computer may
implement any of a variety of operating systems, including UNIX,
Linux, Microsoft Windows, MacOS, or any other operating system.
Computer code for implementing aspects of the present invention may
be written in a variety of languages, including PERL, C, C++, Java,
JavaScript, VBScript, AWK, or any other scripting or programming
language that can be executed on the host computer or that can be
compiled to execute on the host computer. Code may also be written
or distributed in low level languages such as assembler languages
or machine languages.
The host computer system advantageously provides an interface via
which the user controls operation of the tools. In the examples
described herein, software tools are implemented as scripts (e.g.,
using PERL), execution of which can be initiated by a user from a
standard command line interface of an operating system such as
Linux or UNIX. Those skilled in the art will appreciate that
commands can be adapted to the operating system as appropriate. In
other embodiments, a graphical user interface may be provided,
allowing the user to control operations using a pointing device.
Thus, the present invention is not limited to any particular user
interface.
Scripts or programs incorporating various features of the present
invention may be encoded on various computer readable media for
storage and/or transmission. Examples of suitable media include
magnetic disk or tape, optical storage media such as compact disk
(CD) or DVD (digital versatile disk), flash memory, and carrier
signals adapted for transmission via wired, optical, and/or
wireless networks conforming to a variety of protocols, including
the Internet.
In a further aspect, the invention provides computer implemented
methods for determining the presence or absence of cancer
(including but not limited to prostate cancer or the biochemical
reoccurrence of prostate cancer) in an individual. In some
embodiments, the methods comprise: receiving, at a host computer, a
methylation value representing the methylation level of at least
one cytosine within a DNA region in a sample from the individual
where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%,
97%, 98%, or 99% identical to, or comprises, a sequence is selected
from the group consisting of SEQ ID NOS: 1-32; and comparing, in
the host computer, the methylation level to a threshold value,
wherein the threshold value distinguishes between individuals with
and without cancer (including but not limited to prostate cancer or
the biochemical reoccurrence of prostate cancer), wherein the
comparison of the methylation level to the threshold value is
predictive of the presence or absence of cancer (including but not
limited to prostate cancer or the biochemical reoccurrence of
prostate cancer) in the individual.
In some embodiments, the receiving step comprises receiving at
least two methylation values, the two methylation values
representing the methylation level of at least one cytosine
biomarkers from two different DNA regions; and the comparing step
comprises comparing the methylation values to one or more threshold
value(s) wherein the threshold value distinguishes between
individuals with and without cancer (including but not limited to
prostate cancer or the biochemical reoccurence of prostate cancer),
wherein the comparison of the methylation value to the threshold
value is predictive of the presence or absence of cancer (including
but not limited to cancers of the bladder, breast, cervix, colon,
endometrium, esophagus, head and neck, liver, lung(s), ovaries,
prostate, rectum, and thyroid, and melanoma) in the individual.
In another aspect, the invention provides computer program products
for determining the presence or absence of cancer (including but
not limited to prostate cancer or the biochemical reoccurence of
prostate cancer), in an individual. In some embodiments, the
computer readable products comprise: a computer readable medium
encoded with program code, the program code including: program code
for receiving a methylation value representing the methylation
status of at least one cytosine within a DNA region in a sample
from the individual where the DNA region is at least 90%, 91%, 92%,
93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a
sequence selected from the group consisting of SEQ ID NOS: 1-32 and
program code for comparing the methylation value to a threshold
value, wherein the threshold value distinguishes between
individuals with and without cancer (including but not limited to
prostate cancer or the biochemical reoccurence of prostate cancer),
wherein the comparison of the methylation value to the threshold
value is predictive of the presence or absence of cancer (including
but not limited to prostate cancer or the biochemical reoccurence
of prostate cancer), in the individual.
Materials and Methods
Tissues/Nucleic Acid:
Prostate tissues used for this study were collected at Stanford
University Medical Center between 1999 and 2007 with patient
informed consent under an IRB-approved protocol. Tissue samples
were removed from each prostate, flash-frozen, and stored at
-80.degree. C. Tumor tissue samples underwent macro-dissection to
enrich for tumor cell population, and tumor tissues in which at
least 90% of the epithelial cells were cancerous were selected for
nucleic acid extractions. Nucleic acid was extracted from the
tissues using QIAGEN AllPrep DNA/RNA mini kit (QIAGEN).
DNA Methylation Analysis Via Illumina Infinium Human Methylation
450K:
Five hundred nanograms of DNA from each tissue was sodium bisulfate
treated, and DNA methylation levels were assayed using the Illumina
Infinium Human Methylation 450K beadchip array (Illumina). We
calculated the methylation beta score as:
.beta.=Intensity.sub.Methylated/(Intensity.sub.Methylated+Intensity.sub.U-
nmethylated). We converted any data points that were not
significantly above the background intensity to NAs. We removed any
CpG with greater than 10% missing values. In order to correct for
batch effect, we performed a Combat normalization on array chip
number using the ComBat R package. Post-ComBat normalization, we
observed that the Infinium I and II assays showed two distinct
bimodal .beta.-value distributions, so we developed a regression
method to convert the type I and type II assays to a single bimodal
.beta.-distribution corresponding to Reduced Representation
Bisulfite Sequencing (RRBS) .beta.-values. This corrected for the
distinct bimodal distributions and aligned our data with RRBS
values to allow for future integration with RRBS data. We selected
four samples to develop a regression equation to convert Methyl
450K data to RRBS data. We split the Combat normalized Methylation
450K data based on the type I or type II assay giving us 12,687
CpGs for the type I assay and 8,439 CpGs for the type II assay. We
then developed a linear and quadric equation relating the
Methylation 450 type I and type II assays .beta.-values to the RRBS
.beta.-values using least-squares regression. After testing the
equations and visual inspection of the RRBS vs. Methylation 450K
.beta.-values scatter plots, we determined the quadric equation
gave the best fit to the data. The .beta.-value distribution is
fixed at zero and one, thus after the Methylation 450K data was
converted to RRBS .beta.-values using the quadric equations, any
values less than zero were assigned zeros and values greater than
one were assigned ones. The equations for correction are shown
below:
Infinium I to RRBS
RRBS.sub..beta.=0.00209+0.4377.times.Methyl450.sub..beta.+0.6303.times.Me-
thyl450.sub..beta..sup.2
Infinium II to RRBS
RRBS.sub..beta.=-0.01146+0.2541.times.Methyl450.sub..beta.+0.9832.times.M-
ethyl450.sub..beta..sup.2 Discovery of CpG Loci with DNA
Methylation Levels Statistically Associated with Biochemical
Recurrence Using Linear Regression Models:
Prior to any statistical analysis, in order to improve statistical
power, we removed any CpG that had a standard deviation across all
samples less than 0.01, as these CpGs were considered unchanged
across samples. This left us with 347,899 CpGs for the statistical
analysis. We fit the tumor prostate DNA methylation data to a
linear model using the lm function in R. We included several
clinical covariates in the linear model, including patient PSA
level before prostatectomy surgery, patient pathological Gleason
grade, T score (from TNM prostate staging score), N score (from TNM
prostate staging score), whether the patient had positive surgical
margins or not, whether the tumor invaded the seminal vesicals,
whether the tumor invaded the capsule of the prostate, and whether
the patient is biochemically recurrent. At an FDR of 10%, we
discovered 13 CpG loci that had DNA methylation patterns that were
statistically associated with biochemical recurrence. We also fit
the tumor prostate DNA methylation data to a Robust linear model
using the rlm function in R. At an FDR of 5%, filtering out CpGs
that did not converge, we found 1,222 CpG loci that had DNA
methylation patterns that were statistically associated with
biochemical recurrence. Because significant rlm results are prone
to outliers, we further filtered the significant CpGs to highlight
CpGs with the largest methylation differences between the
biochemically recurrent patients and the non-recurrent patients. We
selected, from the 1,222 CpG loci, CpGs with a median methylation
difference between biochemical recurrent patients and non-recurrent
patients of at least 10%, and a Median Absolute Deviation (meaning
the dispersion of the data around the median) no greater than 20%.
This filtering process left us with 5 additional CpGs over the 13
that we discovered through linear regression.
Discovery of CpG Loci with DNA Methylation Levels Statistically
Associated with Biochemical Recurrence Using Survival Analysis:
After the static regression analysis was completed we used survival
analysis to include time to recurrence in our study. The time to
recurrence data was censored; hence we used the Cox proportional
hazards model to study the affect of CpG methylation on recurrence
times. We used the Wald test to determine significant CpGs for
recurrence. We found 1,627 CpGs with an FDR of 0.05. To investigate
all combinations of the 1,627 CpGs would have required 716,490,715
individual models with 3 CpGs, hence we elected to test the 100
most significant CpG s (requiring 161,700 models) from the survival
analysis to determine their predictive power for prostate cancer
recurrence. We then applied the same logistic regression analysis
as used for the linear regression CpGs and identified 14 more CpGs
with a very strong predictive power for prostate cancer
recurrence.
Logistic Regression and Receiver Operating Characteristic (ROC)
Curves:
After the CpGs were identified using linear regression, we used
logistic regression to determine the predictive power of these CpGs
for prostate cancer recurrence. Based on the sample size of 73
tumors, we elected to study all possible combinations of 3
significant CpGs along with Gleason score to determine which
combinations of CpGs provided the best prediction of biochemical
recurrence. We developed a logistic regression model for each of
the 816 combinations of 3 CpGs and Gleason score. For each model we
determined the Akaike information criterion (AIC) to determine the
best predictors. We used the AIC since it judges models based on
how close the fitted values tend to be to the expected values. The
optimal models will minimize the AIC. We then took the models with
the lowest AIC and determined the sensitivity and specificity of
each model. We used the sensitivity and specificity to produce ROC
curves for these models. Since a perfect predictor will have an
area under the ROC curve of 1, we then calculated the area under
the ROC curves and selected the model with the area closest to 1 as
the best model to predict recurrence. The best model had an area of
0.97. To test the ability of the CpGs to predict recurrence we
randomly selected CpGs that were not identified using linear
regression. Using these CpGs we developed logistic regression
models, the ROC curves, and calculated the area under these curves.
For these models the area was close to 0.5, which is the expected
area when a model provides no predicative power.
SEQUENCE LISTINGS
1
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gattcgcgaa gctgtgcaca 60cgtcattttt atcgcaaaaa tgaaacctgc cggtgccgtt
ttaaacagca gagttggctc 120ag 1222122DNAHomo sapiens 2acagcatttg
gattcgcgaa gctgtgcaca cgtcattttt atcgcaaaaa tgaaacctgc 60cggtgccgtt
ttaaacagca gagttggctc agtgtcccct cagtgctttc tttggctttt 120tc
1223122DNAHomo sapiens 3aggtgcctta gttactggac agagaccccg ggttccttag
ttactggaca gagagtgctg 60cgcttcaggt gccatggcct cagtttcctc ctttctgaag
tgatgaaaat acattgtgcg 120cc 1224122DNAHomo sapiens 4tccattggaa
ggtcagttgc agtgcagccc ttttccgggg taataagttt ttaatgtaga 60cgcaattagt
gaataagcag ttgattagtt cattacgaaa atagtgaaaa caacaccatt 120tg
1225122DNAHomo sapiens 5ctccgcggga ctcctgggcc ggcctcgccg cctctccggc
ggggaacctt ccccagcccc 60cgtccgcaca gatccctagc gccccgagcc cccgcccttc
gcgcctaggc gtgcgccggc 120tc 1226122DNAHomo sapiens 6aagaatcttc
cttttctcta gagccaagag aacaggctca aaccaaataa actagccata 60cgatcttcta
aatgccccag tgtgttcaca gtgaacacat tactttcata tgccaaaaag 120tc
1227122DNAHomo sapiens 7ctggagacgt gcacacgcct gggaacaggt gctggggcgg
cacccaagca cgggagccag 60cgttggggaa gcggcacacc ccagggagct agcgctggca
aagcacaccc accaagagtg 120ag 1228122DNAHomo sapiens 8agccaactcc
tcacccgcct ggggactgct tggggcagcc catagtggga gccctggggc 60cgctagattg
aatcactttt ctatttttgg agcagcctat actcccctct gcctctggct 120ta
1229122DNAHomo sapiens 9ggcgtcggga agtgttcttg gtcagagcag agaaactgca
gttgccttgt gctttcttcc 60cggagagccc tgcagctgac cctctgccag agctttgggt
caggctttct gactgaccct 120ta 12210122DNAHomo sapiens 10acagttgtga
gccaccgcgc ccagccacta gttattcact ttcttgtttg aacttttgat 60cgtttattcc
tttactcggc tgcccattca tttgttaata cacttatgaa atattcaatg 120ac
12211122DNAHomo sapiens 11aacccatgag caaacacact cgagtctctc
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120gg 12213122DNAHomo sapiens 13gatgcccact ggataggaat tcaccaattc
tcctctgagt tcattttcta aaggatagag 60cgacaacaca aaatacctta agtaagagaa
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14caagtacagg gttaaatgaa ctaagtaagg catgtaaaca gcttagttta atgtttggcc
60cgtggtatac aatctagcat tagccatcat tattggtaac ccttgtttag gcagcctttc
120tg 12215122DNAHomo sapiens 15ggccaggagc tggaaaggcg ctgagcccag
gtggctttca ctccctgtgt gtgaggccat 60cgctcagcat ctgcacgctg tctgctgcgt
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120ag 12219122DNAHomo sapiens 19tgctgtaacg ttgacttttc aggagtggca
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120gt 12221122DNAHomo sapiens 21tgagggtctc tcaccgagtc tctccttcag
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aacaaggttt tcatgatcga ccgctacctg 120ca 12222122DNAHomo sapiens
22gcctagatgg tggccggtgt ttcctggttg cttactggtc tttctgagtt ctggttcact
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120tg 12223122DNAHomo sapiens 23gtttcagaac agaaggaaga agggaaatga
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aggcagccaa aataacatcc atttaacaag 120ta 12224122DNAHomo sapiens
24agcctggcat cttaatgcag ccagatgcat gaggtcccag ttactcaggc tcctgcagag
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cccagctgct ggctccagcc acgggatgcg 120cc 12226122DNAHomo sapiens
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120tc 12227122DNAHomo sapiens 27aggctgagac tagggcatca aatcctaata
gcctctgttt actgagcaca cagtctgtgc 60cgtgtcctgt gctgaggact tgataggcat
tatctctttt aatcctccca attcctggtg 120gg 12228122DNAHomo sapiens
28tcctggatgg tgggacctac cactgagccc atatgtacag ttgcagtttc cctggttccc
60cgtggcagcc caggaaactg atgcccatcc aaaggtcatt gaccccagag gctcccaaga
120ac 12229122DNAHomo sapiens 29gctctccctc atttgccgtc cccttggctg
tggagcagaa tctccatcga gcagttgctc 60cgctggacaa gctcgttctg gatggggaca
tgaatctacg ctactaccag gacttccgtg 120gc 12230122DNAHomo sapiens
30ctcaaagggt taagcaggtg cctctccggg cgaggcgcta ttggccgagg gctggggcgg
60cgcggccgcg gtcaccacgc ttcgccgatc ccaacttggg ttcctgcgga aggcaaggcg
120gc 12231122DNAHomo sapiens 31ggatttttag gggctcttga gctggaattt
tttggggggc gccgggaggt gtgctggggc 60cgcagacccc atacaggagg taagttagag
aaccacacgc aggggaggga tgctgctgct 120tc 12232122DNAHomo sapiens
32ccggaacacc cgtggtgacc gccgggaccc tgcctgtgac tctccaggac tctgcgaccc
60cgggatggat attgcgatgc tggtctcgac cctgaaaccc tccctcggat ctgtgacctc
120gg 122
* * * * *
References